Generally, since a fingerprint composed of many ridges with striped pattern has two important characteristics of “permanence” and “uniqueness”, it has been used for a person authentication method for a long time. Particularly, matching by using fingerprints left on the crime scene is an effective investigation method.
Recently, in many police agencies, a fingerprint matching system using a computer (a computing machine) has been introduced, and left fingerprint matching has been performed. As objects of the left fingerprint matching, a database of tenprint cards taken from criminals (suspects and arrestees) has been made.
Ten fingers are 10 fingers of both hands. The 5 fingers of the single hand are called a thumb finger, an index finger, a middle finger, a ring finger and a little finger.
As described in a non-patent literature 1 of “The Science of Fingerprints”, the tenprint card includes a total of fourteen kinds of images (14 images) containing ten kinds of rolled prints and four kinds of plane prints or slap prints.
Here, it is assumed that, in the tenprint card, ten kinds of rolled print image frames (ruled line frame) and four kinds of slap print image frames (ruled line frame) are already printed.
The rolled print images are fingerprints of a thumb finger, an index finger, a middle finger, a ring finger and a little finger of both hands individually imprinted while these fingers are rolled from side to side, and are images widely taken containing right and left side portion regions of the fingers by rolling the fingers. That is, the rolled print images include a total of ten images (ten kinds of rolled print images) that are two images of rolled prints of a thumb finger, an index finger, a middle finger, a ring finger and a little finger of both hands.
The slap print images are images taken containing fingertip region of the fingers by not rolling the fingers from side to side but standing the fingers at front. The slap prints include: a thumb-finger slap (a slap print of a thumb finger) that a fingerprint of the thumb finger is individually imprinted; and a four-finger slap (four kinds of slap prints) that fingerprints of the other four fingers (an index finger, a middle finger, a ring finger and a little finger) are simultaneously imprinted. That is, the slap print images includes a total of four images that are two images of the thumb-finger slaps of the right and left hands and two images of the four-finger slaps of the right and left hands.
Accordingly, the tenprint card has the fourteen kinds of images and the rolled prints of ten fingers and the slap prints of ten fingers are printed in it.
In a non-patent literature 2 of “ANSI/NIST-ITL-1-2000 Data Format for the Interchange of Fingerprint, Facial, & Scar Mark & Tattoo (SMT) Information” standardized in U.S. NIST (National Institute of Standards and Technology), a rule for creating a digital image by scanning a fingerprint image on the tenprint card is regulated.
This rule defines that fourteen individual images are created as digital images by segmenting the images along image frames on the tenprint card.
Furthermore, in this explanation, fingerprint images created as digital images with resolution of “500 ppi” are used based on this rule.
The slap print images are also used for checking a finger position error (an error of imprinting position) of the rolled prints by comparing the slap print images with the rolled print images.
The error of imprinting position is also called an error of imprinting sequence and this check is called a sequence check. The sequence check executed by a computer has started from the 1990's. This starts with segmenting individual fingers from the four-finger slap image. Segmenting the individual fingers from the four-finger slap is called slap fingerprint segmentation or slap segmentation.
A non-patent literature 3 of “NISTIR 7209 Slap Fingerprint Segmentation Evaluation 2004 (SlapSeg04 Analysis Report)” issued by U.S. NIST describes accuracy and problems regarding the slap fingerprint segmentation.
Recent fingerprint matching system is aimed at improving a hit rate of a left fingerprint by registering not only ten fingers of the rolled print images on the tenprint card but also ten fingers of the slap print images in the database and using them as objects for left fingerprint matching.
As described in the non-patent literature 1, appropriate fingerprint detection is that fingerprints are imprinted completely inside the image frames (the ruled line frames) printed on the tenprint card. However, since a criminal who is fingerprinted may be uncooperative for taking fingerprints, the taken fingerprint images may be protruded from the frames without appropriately imprinted inside the frames.
As a related art, a patent literature 1 (Japanese patent publication JP-Heisei 07-168925A) discloses a tenprint card input device. The tenprint card input device includes: a tenprint card image input section inputting image data of a tenprint card by an image scanner and so on; a data process section; an image storage section; a display device; a pointing device; and a segmented fingerprint image output section outputting fingerprint image data segmented in units of fingers. A segmentation information input section of the data process section memorizes the input tenprint card image data into the image storage section, overlaps ten segmentation frames for specifying respective segmentation ranges with the tenprint card image to displays them on the display device, and makes an operator input segmentation information for each finger. A segmentation editor section segments fingerprint images of respective fingers from the tenprint card image data of the image storage section based on the segmentation information, and edits and outputs the segmented fingerprint image.
In addition, a patent literature 2 (Japanese patent publication JP2003-173445A) discloses a fingerprint matching method and device. This related technique extracts the area of a region as core line stability, the region containing no feature point which is composed of points that a ridge of a fingerprint pattern diverges and points that a ridge ends and the region including a predetermined attention point as a center of the region, and then uses the region for matching.
Furthermore, a patent literature 3 (Japanese patent publication JP2004-078434A) discloses a striped pattern image appraising device and a striped pattern image. In this related technique, a feature point data matching section creates information of pair feature points. A core line data matching section creates information of core line points of a search side and a file side which make a pair. An image deformation correction section corrects data of the file side and reduces deformation of an image by using not only the information of the pair feature points but also the information of the core line points making a pair. An appraisive image edition display section outputs both of data of the search side and the corrected data of the file side in order to easily appraise the data. For example, the section overlaps the data of the search side with the corrected data of the file side and outputs them.
Moreover, a patent literature 4 (Japanese patent publication JP 2008-040693A) discloses a line noise removing device, a line noise removing method and a line noise removing program. This line noise removing device includes an image binarization section, a line noise certainty factor calculation section, a line noise region determination section, a density conversion section and an image synthesis section. The image binarization section creates a binary image by binarizing an input image. The line noise certainty factor calculation section creates a rotation image which the binary image is rotated for each of a plurality of rotation angles, calculates an edge feature value for each region continuing black pixels of each rotation image, and calculates a line noise certainty factor. The line noise region determination section selects a rotation angle candidate from the rotation angles, and determines a line noise region based on the line noise certainty factor, for the rotation image corresponding to each rotation angle candidate. The density conversion section creates a density conversion image by executes a local image enhancement on the region corresponding to the line noise region of the input image. The image synthesis section creates a synthesis image by synthesizing the density conversion images when a plurality of the rotation candidates is present.